Fire is one of the incidents that disturbs homeowners because of the fire that will drain property and can claim lives when there is a lack of anticipation and minimal ignorance of the incident, such as when a fire occurs there is no early warning to the homeowner, this is also a cause of fire. The cause of the fire can be caused by a gas leak, electrical short circuit or caused by the community itself. Based on these problems, a solution is needed to create a safety system technology to be applied to the home kitchen, namely the design of an early detection system for fires based on the Internet Of Things (IoT) using ESP8266 which is useful for monitoring kitchen conditions and will automatically send a notification of the kitchen condition if the sensor reads an incident. The Hardware Design uses several tools consisting of ESP8266, Arduino R3. Jumper Cables, Fire Sensors, MQ-2 Gas Sensors, Mini Fans, Mini Water Pumps, Relays, Buzzers. and for the software design using Arduino IDE for system programming and Blynk as an application to display notifications. The designed system will be tested using black box testing which is used to determine whether the designed system will meet the specified parameters or not, and the results of the design state that the system is in accordance with the parameters used.

Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.

This research focuses on evaluating the Combox web application, a digital tool designed to help Food and Beverage (F&B) business owners strengthen their online presence. The analysis was carried out through Black Box Testing, specifically using the Equivalence Partitioning method, to assess core functionalities like login, logout, product management, and pagination. The findings reveal that while most features function as intended, there are issues with product addition and editing, as well as pagination when no data is available. These results highlight areas that need refinement to improve the application’s reliability and user experience. In summary, this research supports the advancement of a digital platform that enables F&B businesses to harness technology effectively in today’s competitive landscape.

Smart grids incorporate IoT devices that enhance energy management, monitoring, and overall grid efficiency. However, this interconnectivity also increases vulnerability to cybersecurity threats, posing risks to critical infrastructure. This research investigates the implementation of blockchain technology to secure data transactions within IoT-based smart grids. By leveraging blockchain's decentralized, tamper-resistant characteristics, the study demonstrates improvements in data integrity and cybersecurity for smart grids, providing a potential framework for resilient and secure energy infrastructures.

CV. Aren Jaya is an AC service provider located in South Tangerang. In 2017 this shop began to develop by opening new and used AC sales services, AC installation services, used washing machine sales services and washing machine service services. In carrying out the company's business process activities, of course there will always be possible risks and potential risks that can threaten and disrupt business process activities. The need for a risk analysis of existing IS/IT resources in the company, using the ISO 31000:2018 method related to risk management. The purpose of this study is to minimize all possible risks that are currently being experienced or will also occur and provide appropriate recommendations regarding risks that may occur at any time. The research method used in the risk analysis uses the ISO 31000:2018 framework. The results of this risk analysis are in the form of an analysis of the possibility of existing risks, evaluations and risk mitigation plans so that they can produce improvements to existing risks. The final result of the research produces risk recommendations, so the company can adjust to the priorities of the existing risk level, so that it does not interfere with business activities at CV. Arena Jaya.
 

Cloud-based systems are increasingly vulnerable to a range of cybersecurity threats, driving the need for advanced encryption methods. This paper investigates the potential of quantum cryptography in securing cloud environments, focusing on the use of quantum key distribution (QKD) protocols. By leveraging the principles of quantum mechanics, the study demonstrates significant improvements in data security, offering enhanced protection against eavesdropping and paving the way for more resilient cloud security frameworks.

Power systems are critical infrastructure that face significant challenges due to increasing demand and inherent complexity. Predicting failures in power systems is crucial for enhancing grid reliability, minimizing downtime, and optimizing maintenance processes. This study evaluates various deep learning models, specifically convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models, for predicting power system failures. By analyzing these models’ performance metrics on historical power grid data, the study provides insights into the strengths and weaknesses of each approach. The findings contribute to the development of more robust predictive models for power system reliability.

Accurate image segmentation is a pivotal process in medical imaging, essential for supporting diagnosis, treatment planning, and monitoring disease progression. This study evaluates the effectiveness of machine learning algorithms, including U-Net, Fully Convolutional Networks (FCNs), and Mask R-CNN, in achieving high-precision segmentation of medical images. Experimental results demonstrate that these models significantly enhance segmentation accuracy, enabling more precise diagnostic outcomes in clinical settings and advancing the development of automated medical imaging technologies.

Energy efficiency is a critical concern in wireless sensor networks (WSNs) due to the limited power resources available in sensor nodes. Prolonging network lifespan while ensuring reliable data transmission is essential for successful deployment in various applications, such as environmental monitoring, military operations, and industrial automation. This paper presents a mathematical model designed to optimize energy consumption across various nodes in WSNs. By implementing simulations and analyzing data from these models, the study demonstrates significant improvements in extending network lifespan while maintaining reliable data throughput. The findings contribute valuable insights into energy management for large-scale sensor deployments.

Industrial IoT (IIoT) networks, critical for automation and smart manufacturing, are susceptible to faults due to their complexity and the large number of connected devices. This paper introduces a deep learning-based approach for early fault detection in IIoT networks. By leveraging recurrent neural networks (RNNs) and convolutional neural networks (CNNs), the system effectively identifies anomalies in real-time, helping to reduce system downtime and enhance operational efficiency in industrial settings.